Semantic Segmentation of Node and Edge Diagrams for Assistive Technology
Quick Answer
This paper introduces novel deep learning models for semantic segmentation of node-link diagrams, achieving over 93% per-pixel accuracy on a large synthetic dataset.
Quick Take
This paper introduces novel deep learning models for semantic segmentation of node-link diagrams, achieving over 93% per-pixel accuracy on a large synthetic dataset. These models enhance accessibility for assistive technologies, addressing the challenge of interpreting bitmap representations of complex diagrams.
Key Points
- Models achieve over 93% accuracy in semantic segmentation of node-link diagrams.
- Focus on improving accessibility for non-visual users of complex diagrams.
- Addresses limitations of existing assistive interfaces reliant on machine-readable formats.
- Utilizes a large synthetic dataset for robust model training and evaluation.
- Promotes better understanding of mathematical graphs and flowcharts.
Paper Resources
Article Excerpt
From source RSS / original summaryarXiv:2606. 11320v1 Announce Type: new Abstract: In this paper, we present a novel set of related models for semantic segmentation of node-link diagrams. These diagrams are frequently used to represent mathematical graphs, relationships between concepts, and flowcharts.
Such diagrams are difficult to access non-visually; while some assistive interfaces have been designed for node-link diagrams, they rely upon a machine-readable representation of the diagram, whereas such diagrams will generally be made available as bitmap images. Our compact deep learning models show excellent quantitative and qualitative performance on a large synthetic dataset of node-link diagrams, reaching per-pixel accuracy over 93\%.
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